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A Hybrid Quantum–Classical Convolutional Neural Network for Enhanced Image Classification

by Deepika D., Vanishree K., Munishamaiah Krishna
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 66
Year of Publication: 2025
Authors: Deepika D., Vanishree K., Munishamaiah Krishna
10.5120/ijca2025926113

Deepika D., Vanishree K., Munishamaiah Krishna . A Hybrid Quantum–Classical Convolutional Neural Network for Enhanced Image Classification. International Journal of Computer Applications. 187, 66 ( Dec 2025), 28-34. DOI=10.5120/ijca2025926113

@article{ 10.5120/ijca2025926113,
author = { Deepika D., Vanishree K., Munishamaiah Krishna },
title = { A Hybrid Quantum–Classical Convolutional Neural Network for Enhanced Image Classification },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2025 },
volume = { 187 },
number = { 66 },
month = { Dec },
year = { 2025 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number66/a-hybrid-quantumclassical-convolutional-neural-network-for-enhanced-image-classification/ },
doi = { 10.5120/ijca2025926113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-12-18T17:50:24.506471+05:30
%A Deepika D.
%A Vanishree K.
%A Munishamaiah Krishna
%T A Hybrid Quantum–Classical Convolutional Neural Network for Enhanced Image Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 66
%P 28-34
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this research is to develop a Hybrid Quantum–Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance. The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity. Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%. Statistical analysis confirmed the significance of these improvements. Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model’s robustness. The results highlight the hybrid QC-CNN’s potential for efficient and scalable quantum-enhanced deep learning applications. Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.

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Index Terms

Computer Science
Information Sciences

Keywords

Quantum Machine Learning Hybrid Quantum–Classical CNN Parameterized Quantum Circuits Image Classification Noise Resilience

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